Combining spectroscopic and photometric surveys using angular cross-correlations II: Parameter constraints from different physical effects

Future spectroscopic and photometric surveys will measure accurate positions and shapes of an increasing number of galaxies. In the previous paper of this series we studied the effects of Redshift Space Distortions (RSD), baryon acoustic oscillations (BAO) and Weak gravitational Lensing (WL) using angular cross-correlation. Here, we provide a new forecast that explores the contribution of including different observables, physical effects (galaxy bias, WL, RSD, BAO) and approximations (non-linearities, Limber approximation, covariance between probes). The radial information is included by using the cross-correlation of separate narrow redshift bins. For the auto correlation the separation of galaxy pairs is mostly transverse, while the cross-correlations also includes a radial component. We study how this information adds to our figure of merit (FoM), which includes the dark energy equation of state $w(z)$ and the growth history, parameterized by $\gamma$. We show that the Limber approximation and galaxy bias are the most critical ingredients to the modelling of correlations. Adding WL increases our FoM by 4.8, RSD by 2.1 and BAO by 1.3. We also explore how overlapping surveys perform under the different assumption and for different figures of merit. Our qualitative conclusions depend on the survey choices and scales included, but we find some clear tendencies that highlight the importance of combining different probes and can be used to guide and optimise survey strategies

Implications of a wavelength dependent PSF for weak lensing measurements

The convolution of galaxy images by the point-spread function (PSF) is the dominant source of bias for weak gravitational lensing studies, and an accurate estimate of the PSF is required to obtain unbiased shape measurements. The PSF estimate for a galaxy depends on its spectral energy distribution (SED), because the instrumental PSF is generally a function of the wavelength. In this paper we explore various approaches to determine the resulting `effective' PSF using broad-band data. Considering the Euclid mission as a reference, we find that standard SED template fitting methods result in biases that depend on source redshift, although this may be remedied if the algorithms can be optimised for this purpose. Using a machine-learning algorithm we show that, at least in principle, the required accuracy can be achieved with the current survey parameters. It is also possible to account for the correlations between photometric redshift and PSF estimates that arise from the use of the same photometry. We explore the impact of errors in photometric calibration, errors in the assumed wavelength dependence of the PSF model and limitations of the adopted template libraries. Our results indicate that the required accuracy for Euclid can be achieved using the data that are planned to determine photometric redshifts

Cosmological constraints from multiple tracers in spectroscopic surveys

We use the Fisher matrix formalism to study the expansion and growth history of the Universe using galaxy clustering with 2D angular cross-correlation tomography in spectroscopic or high resolution photometric redshift surveys. The radial information is contained in the cross correlations between narrow redshift bins. We show how multiple tracers with redshift space distortions cancel sample variance and arbitrarily improve the constraints on the dark energy equation of state $\omega(z)$ and the growth parameter $\gamma$ in the noiseless limit. The improvement for multiple tracers quickly increases with the bias difference between the tracers, up to a factor $\sim4$ in $\text{FoM}_{\gamma\omega}$. We model a magnitude limited survey with realistic density and bias using a conditional luminosity function, finding a factor 1.3-9.0 improvement in $\text{FoM}_{\gamma\omega}$ -- depending on global density -- with a split in a halo mass proxy. Partly overlapping redshift bins improve the constraints in multiple tracer surveys a factor $\sim1.3$ in $\text{FoM}_{\gamma\omega}$. This findings also apply to photometric surveys, where the effect of using multiple tracers is magnified. We also show large improvement on the FoM with increasing density, which could be used as a trade-off to compensate some possible loss with radial resolution.Comment: 20 pages, 15 figure

Combining spectroscopic and photometric surveys using angular cross-correlations I: Algorithm and modelling

Weak lensing (WL) clustering is studied using 2D (angular) coordinates, while redshift space distortions (RSD) and baryon acoustic oscillations (BAO) use 3D coordinates, which requires a model dependent conversion of angles and redshifts into comoving distances. This is the first paper of a series, which explore modelling multi-tracer galaxy clustering (of WL, BAO and RSD), using only angular (2D) cross-correlations in thin redshift bins. This involves evaluating many thousands cross-correlations, each a multidimensional integral, which is computationally demanding. We present a new algorithm that performs these calculations as matrix operations. Nearby narrow redshift bins are intrinsically correlated, which can be used to recover the full (radial) 3D information. We show that the Limber approximation does not work well for this task. In the exact calculation, both the clustering amplitude and the RSD effect increase when decreasing the redshift bin width. For narrow bins, the cross-correlations has a larger BAO peak than the auto-correlation because smaller scales are filtered out by the radial redshift separation. Moreover, the BAO peak shows a second (ghost) peak, shifted to smaller angles. We explore how WL, RSD and BAO contribute to the cross-correlations as a function of the redshift bin width and present a first exploration of non-linear effects and signal-to-noise ratio on these quantities. This illustrates that the new approach to clustering analysis provides new insights and is potentially viable in practice

Filling in CMB map missing data using constrained Gaussian realizations

For analyzing maps of the cosmic microwave background sky, it is necessary to mask out the region around the galactic equator where the parasitic foreground emission is strongest as well as the brightest compact sources. Since many of the analyses of the data, particularly those searching for non-Gaussianity of a primordial origin, are most straightforwardly carried out on full-sky maps, it is of great interest to develop efficient algorithms for filling in the missing information in a plausible way. We explore practical algorithms for filling in based on constrained Gaussian realizations. Although carrying out such realizations is in principle straightforward, for finely pixelized maps as will be required for the Planck analysis a direct brute force method is not numerically tractable. We present some concrete solutions to this problem, both on a spatially flat sky with periodic boundary conditions and on the pixelized sphere. One approach is to solve the linear system with an appropriately preconditioned conjugate gradient method. While this approach was successfully implemented on a rectangular domain with periodic boundary conditions and worked even for very wide masked regions, we found that the method failed on the pixelized sphere for reasons that we explain here. We present an approach that works for full-sky pixelized maps on the sphere involving a kernel-based multi-resolution Laplace solver followed by a series of conjugate gradient corrections near the boundary of the mask.Comment: 22 pages, 14 figures, minor changes, a few missing references adde